Iyer Kartik K, Angwin Anthony J, Van Hees Sophia, McMahon Katie L, Breakspear Michael, Copland David A
UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston QLD, Brisbane, Australia; School of Health & Rehabilitation Sciences, The University of Queensland, St Lucia, QLD, Brisbane, Australia.
School of Health & Rehabilitation Sciences, The University of Queensland, St Lucia, QLD, Brisbane, Australia; School of Audiology and Speech Sciences, The University of British Columbia, Vancouver, BC, Canada.
Cortex. 2020 Apr;125:30-43. doi: 10.1016/j.cortex.2019.12.017. Epub 2019 Dec 30.
Predicting aphasia recovery is difficult due to a high variability in treatment response. Detailed measures of treatment response are compounded by a dearth of information that examine brain connections that contribute to clinical improvement. In this study we measure alterations to cortical connectivity pathways during a therapy paradigm to detect whether key brain connections that contribute to language recovery can be detected prior to therapy.
We conducted a case-control trial with twenty-three adults including eight adults with chronic, post-stroke aphasia. Aphasia patients underwent 12 naming therapy sessions over 4 weeks, consisting of semantic and phonological treatment approaches. High-density electroencephalography (128 channel EEG) was measured prior to therapy and immediately following treatment in patients with aphasia. Analysis via a dynamic causal modelling (DCM) was used to assess which cortical connections significantly correlated with therapy response.
Altered cortical responses in aphasia patients measured bilaterally in a dual stream DCM connectivity model were predictive of treatment-induced improvement in naming. Pre-treatment DCM coupling (i.e., strength of cortical connections) significant correlated with naming improvement for items treated with semantic therapy, as indicated by increased connection strengths between left inferior parietal lobule (LIPL) and inferior frontal gyrus (LIFG, r = .63, p = .016). In particular, the mediating role of contralateral regions significantly influences overall treatment improvement in the latter stages of stroke recovery.
Our findings identify a potential means to stratify larger cohorts of patients in neurorehabilitation settings into distinct treatments that are tailored to their individual language deficit.
由于治疗反应存在高度变异性，预测失语症恢复情况较为困难。治疗反应的详细测量因缺乏关于促成临床改善的脑连接的信息而变得复杂。在本研究中，我们测量了治疗范式期间皮质连接通路的变化，以检测在治疗前是否能够检测到促成语言恢复的关键脑连接。
我们对23名成年人进行了一项病例对照试验，其中包括8名患有慢性中风后失语症的成年人。失语症患者在4周内接受了12次命名治疗，包括语义和语音治疗方法。对失语症患者在治疗前和治疗后立即进行高密度脑电图（128通道脑电图）测量。通过动态因果模型（DCM）分析来评估哪些皮质连接与治疗反应显著相关。
在双流DCM连接模型中双侧测量的失语症患者皮质反应改变可预测命名方面的治疗诱导改善。治疗前DCM耦合（即皮质连接强度）与语义治疗项目的命名改善显著相关，左侧顶下小叶（LIPL）和额下回（LIFG）之间的连接强度增加表明了这一点（r = 0.63，p = 0.016）。特别是，对侧区域的中介作用在中风恢复后期显著影响总体治疗改善。
我们的研究结果确定了一种潜在方法，可将神经康复环境中更大队列的患者分层为针对其个体语言缺陷量身定制的不同治疗方法。
